Recently,　artificial　intelligence-based　applications　are　universally　acknowledged.　Digit　recognition,　particularly　Persian/Arabic　handwritten　digits,　has　many　applications　in　today＇s　commercial　contexts　for　example　office　automation　and　document　processing.　However,　researcher　are　struggling　in　hand-crafted　digit　scripts　due　to　the　presence　of　different　digit　writing　patterns,　cursive　nature　and　lack　of　large　public　databases　that　make　the　feature　extraction　process　more　complex.　Therefore,　critical　investigation　is　needed　to　reduce　these　challenges.　In　this　study,　a　modified　Deep　Convolutional　Neural　Network　(DCNN)　architecture　using　three　convolutional　layers　blocks　based　on　convolution,　batch　normalization,　pooling,　fully　connected　and　dropout　regularization　parameters　are　employed　to　hinder　overfitting　and　increase　generalization　performance　is　proposed　to　recognize　handwritten　digits.　Initially,　digits　taken　from　the　HODA　database　are　pre-processed　using　various　steps　including　smoothing,　black　and　white　images　to　grayscale　intensity　images　conversion　and　resizing　it　to　a　fixed　dimension.　Then　optimal　features　extraction　and　recognition　of　handwritten　images　are　done　by　the　DCNN　algorithm.　In　deep　learning　domain,　optimization　algorithms　are　considered　core　solution　and　their　performances　highly　depends　on　optimization　algorithm　selection.　In　this　paper,　various　optimization　algorithms　such　as　stochastic　gradient　descent　(SGD),　Adam,　Adadelta,　Adagrad,　Adamax,　Momentum,　RMSprop　and　Nag　are　employed　for　the　optimization　of　proposed　DCNN.　Moreover,　current　research　also　analyzes　the　role　of　different　epochs　to　ameliorate　optical　character　recognition　(OCR)　performance　of　Persian/Arabic　handwritten　digits.　At　the　end,　we　also　worked　on　finding　a　suitable　composition　of　learning　parameters　to　establish　high　performance　DCNN　architecture　that　conquer　the　loopholes　of　traditional　methods.　Results　reveal　that　the　proposed　DCNN　model　achieves　state-of-the-art　performance　and　outperform　other　studies　in　the　literature.